Driving Rhythm Method for Driving Comfort Analysis on Rural Highways

  • Bo Yu Tongji University
  • Yuren Chen Tongji University
Keywords: driving comfort, driver’s visual lane model, driving rhythm, BP neural network, wavelet transform,

Abstract

Driving comfort is of great significance for rural highways, since the variation characteristics of driving speed are comparatively complex on rural highways. Earlier studies about driving comfort were usually based on the actual geometric road alignments and automobiles, without considering the driver’s visual perception. However, some scholars have shown that there is a discrepancy between actual and perceived geometric alignments, especially on rural highways. Moreover, few studies focus on rural highways. Therefore, in this paper the driver’s visual lane model was established based on the Catmull-Rom spline, in order to describe the driver’s visual perception of rural highways. The real vehicle experiment was conducted on 100 km rural highways in Tibet. The driving rhythm was presented to signify the information during the driving process. Shape parameters of the driver’s visual lane model were chosen as input variables to predict the driving rhythm by BP neural network. Wavelet transform was used to explore which part of the driving rhythm is related to the driving comfort. Then the probabilities of good, fair and bad driving comfort can be calculated by wavelets of the driving rhythm. This work not only provides a new perspective into driving comfort analysis and quantifies the driver’s visual perception, but also pays attention to the unique characteristics of rural highways.

Author Biographies

Bo Yu, Tongji University
A PH.D Candidate in School of Transportation Engineering, Tongji University, has been studying on the research field about traffic safety and road design
Yuren Chen, Tongji University
A professor in School of Transportation Engineering, Tongji University, has been researching on traffic safety, road design and computer aided design.

References

Hassan Y, Sarhan M. Operational effects of drivers’ misperception of horizontal curvature. Journal of Transportation Engineering. 2012;138(11):1314-1320.

Gårder P. Segment characteristics and severity of head-on crashes on two-lane rural highways in Maine. Accident Analysis & Prevention. 2006;38(4):652-661.

Kashani AT, Mohaymany AS. Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Safety Science. 2011;49(10):1314-1320.

Traffic Management Bureau of Ministry of Public Security. 2014 Road Traffic Accident Statistics Report PRC; 2015.

Yang Y, Ren W, Chen L, et al. Study on ride comfort of tractor with tandem suspension based on multi-body system dynamics. Applied Mathematical Modelling. 2009;33(1):11-33.

Gao H, Sun W, Shi P. Robust sampled-data control for vehicle active suspension systems. IEEE Transactions on Control Systems Technology. 2010;18(1):238-245.

Els PS, Theron NJ, Uys PE, et al. The ride comfort vs. handling compromise for off-road vehicles. Journal of Terramechanics, 2007;44(4):303-317.

Múčka P, Granlund J. Is the road quality still better?. Journal of Transportation Engineering. 2012;138(12):1520-1529.

Mayora JMP, Piña RJ. An assessment of the skid resistance effect on traffic safety under wet-pavement conditions. Accident Analysis & Prevention. 2009;41(4):881-886.

Awadallah F. Theoretical analysis for horizontal curves based on actual discomfort speed. Journal of transportation engineering. 2005;131(11):843-850.

Els PS. The applicability of ride comfort standards to off-road vehicles. Journal of Terramechanics. 2005;42(1):47-64.

Fwa TF, Chan WT, Sim YP. Optimal vertical alignment analysis for highway design. Journal of transportation engineering. 2002;128(5):395-402.

Hamdar SH, Qin L, Talebpour A. Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework. Transportation Research Part C: Emerging Technologies. 2016;67:193-213.

Baykal O. Concept of lateral change of acceleration. Journal of Surveying Engineering. 1996;122(3):132-141.

Yang S, Xu J, Yang Z, Pan B. The lateral change of acceleration in evaluation and control of highway design. Journal of Xi’an Highway University. 2001;21(1):46-48.

Yu B, Chen Y. Driving Comfort Evaluation of Urban Road from Driver’s Visual Perception. 15th COTA International Conference of Transportation Professionals; 2015.

Ko J, Guensler R, Hunter M. Analysis of effects of driver/vehicle characteristics on acceleration noise using GPSequipped vehicles. Transportation research part F: Traffic Psychology and Behaviour. 2010;13(1):21-31.

Greenwood ID, Dunn RC, Raine RR. Estimating the effects of traffic congestion on fuel consumption and vehicle emissions based on acceleration noise. Journal of Transportation Engineering. 2007;133(2):

-104.

Hassan Y, Sayed T, Bidulka S. Influence of vertical alignment on horizontal curve perception: Phase II: Modeling perceived radius. Transportation Research Record: Journal of the Transportation Research Board. 2002;1796:24-34.

Hassan Y, Easa SM. Effect of vertical alignment on driver perception of horizontal curves. Journal of transportation engineering. 2003;129(4):399-407.

Wang Y, Shen D, Teoh EK. Lane detection using spline model. Pattern Recognition Letters. 2000;21(8):677-689.

Summala H. Latencies in vehicle steering: experimental studies on drivers’ behavior on the road. No. B2 1981 Monograph; 1981.

Zhao K, Meuter M, Nunn C, et al. A novel multi-lane detection and tracking system. Intelligent Vehicles Symposium (IV), 2012 IEEE. IEEE; 2012. p. 1084-1089.

Yu B, Chen Y, Wang R, et al. Safety reliability evaluation when vehicles turn right from urban major roads onto minor ones based on driver's visual perception. Accident Analysis & Prevention. 2016;95(Pt B):487-494.

Chen Y, Yu B, He S. Coordination between highway horizontal and vertical alignments based on driver’s visual perception deviation. Journal of Tongji University (Natural Science). 2015;43(9):1347-1354.

Kamarianakis Y, Vouton V. Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transportation Research Record. 2003;1857(1):74-84.

Zeng Q, Huang H. A stable and optimized neural network model for crash injury severity prediction. Accident Analysis & Prevention, 2014;73:351-358.

Pal S, Mitra M. Detection of ECG characteristic points using multiresolution wavelet analysis based selective coefficient method. Measurement. 2010;43(2):255-261.

Zhu K, Wong YS, Hong GS. Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. International Journal of Machine Tools and Manufacture. 2009;49(7):537-553.

Published
2017-08-28
How to Cite
1.
Yu B, Chen Y. Driving Rhythm Method for Driving Comfort Analysis on Rural Highways. PROMET [Internet]. 2017Aug.28 [cited 2019Aug.19];29(4):371-9. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/2217
Section
Articles